Comparative‎ ‎Study‎ ‎of‎ ‎an‎ ‎Ensemble‎ ‎Machine Learning‎ ‎Model Versus‎ ‎Maximum‎ ‎Likelihood Model‎ ‎to Assess‎ ‎Reliability Measures in Right Censored Data Analysis

Document Type : Original Scientific Paper

Authors

1 ‎Department of Statistics, Faculty of Mathematical Science, ‎University of Kashan, Kashan‎, ‎I‎. ‎R‎. ‎Iran

2 ‎Department of Computer Science, ‎Faculty of Mathematical Science, ‎University of Kashan, ‎Kashan‎, ‎I‎. ‎R‎. ‎Iran

10.22052/mir.2024.254968.1465

Abstract

‎This paper explores the estimation of a new power function under Type-II right censoring using two methods‎: ‎maximum likelihood estimation (MLE) and an ensemble machine learning model based on stacking‎. ‎The study aims to assess both methods' effectiveness in estimating various reliability measures‎, ‎such as hazard rate‎, ‎mean residual life‎, ‎variance residual life‎, ‎mean inactivity time‎, ‎and variance inactivity time‎. ‎The stacking model integrates five base models‎, ‎radial basis function neural network‎, ‎random forest‎, ‎Support Vector Regression (SVR)‎, ‎Multilayer Perceptron (MLP)‎, ‎and gradient boosting regression trees‎, ‎with an radial basis function neural network serving as a meta-learner for final predictions‎. ‎Numerical experiments compare the performance of the stacking model against MLE for Type-II censored data‎. ‎Results indicate that the stacking model significantly enhances the accuracy of reliability measure predictions‎, ‎showcasing its potential as a robust tool for reliability analysis in the context of Type-II censoring‎.

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References

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Mathematics Interdisciplinary Research
Volume 10, Issue 3
In progress
September 2025
Pages 267-294

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